Conventionally used method for associating human faces in a plurality of images as the same person is to detect the facial part of a person from each image, extract a feature value (e.g., a group of features useful for face recognition, such as the distance between facial parts like eyes, mouth, etc.) of a face from each facial part, and perform clustering based on the feature values thus extracted.
Accordingly, people having similar feature values of faces are grouped into the same cluster, whereas people having dissimilar feature values of faces are separated into different clusters. This makes it possible to judge the sameness of people based on whether the people belong to the same cluster and, if the people are judged to be the same person, to associate the faces of the people with each other.
However, the face of a person changes over time due to the growth, aging, etc. of the person. Therefore, in the case where clustering is performed simply based on the feature values of faces, images that each include the face of the same person but were captured at a very different stage of the person's life may be separated into different clusters.
A known technology for associating people who were judged to be the same person, in consideration of the change in growth, aging, etc., is to judge the sameness of people with use of statistical data obtained by statistically calculating the change of a feature value due to the aging of a human (see Patent Literature 1, for example).